Non-isothermal compositional two-phase flow is considered to be one of the fundamental physical processes in the field of water resources research. The strong non-linearity and discontinuity emerging from phase transition phenomena pose a serious challenge for numerical modeling. Recently, Lauser et al.[1] has proposed a numerical scheme, namely the Nonlinear Complementary Problem (NCP), to handle this strong non-linearity. In this work, the NCP is implemented at both local and global levels of a Finite element algorithm. In the former case, the NCP is integrated into the local thermodynamic equilibrium calculation. While in the latter one, it is formulated as one of the governing equations. The two different formulations have been investigated through several well established benchmarks and analyzed for their efficiency and robustness. In the second part of the thesis, the presented numerical formulations are applied for application and process studies in the context of nuclear waste disposal in Switzerland. Application studies comprehend the coupling between multiphase transport model and complex bio-geo-chemical process to investigate the degradation of concrete material due to two major reactions: carbonation and Aggregate Silica Reaction(ASR). The chemical processes are simplified into a lookup table and cast into the transport model via source and sink term. The efficiency and robustness of the look-up table are further compared with a fully reactive transport model.

The present study proposes to use a Conrad probe for transient growth study by measuring the two-dimensional velocity behind an array of roughness elements in the Blasius boundary layer. A look-up table approach is proposed to increase the accuracy of the data reduction process at low velocities, based on the results of the calibration performed in a round jet. A velocity correction method is proposed to minimize the errors due to high velocity shear and wall-proximity in the Blasius boundary layer by comparing Conrad probe results to previous hot-wire data. Measurements of the steamwise velocity perturbation obtained with the Conrad probe agree with previous studies. The measured spanwise perturbation confirms the transport process suggested by simulation. The results show that the perturbation amplitude increases by increasing both the freestream velocity and roughness elements height. However, the mechanism for changing the perturbation amplitude of influence differs for these two parameters.

The present study proposes to use a Conrad probe for transient growth study by measuring the two-dimensional velocity behind an array of roughness elements in the Blasius boundary layer. A look-up table approach is proposed to increase the accuracy of the data reduction process at low velocities, based on the results of the calibration performed in a round jet. A velocity correction method is proposed to minimize the errors due to high velocity shear and wall-proximity in the Blasius boundary layer by comparing Conrad probe results to previous hot-wire data. Measurements of the steamwise velocity perturbation obtained with the Conrad probe agree with previous studies. The measured spanwise perturbation confirms the transport process suggested by simulation. The results show that the perturbation amplitude increases by increasing both the freestream velocity and roughness elements height. However, the mechanism for changing the perturbation amplitude of influence differs for these two parameters.

In modern wireless communication systems, advanced modulation techniques are used to support more users by handling high data rates and to increase the utilization efficiency of the limited RF spectrum. These techniques are sensitive to the nonlinear distortions due to their high peak to average power ratios. Main source of nonlinear distortion in transmitter topologies are power amplifiers that determine the overall efficiency and linearity of the transmitter. To increase linearity without sacrificing efficiency, power amplifier linearization techniques may be a choice. Baseband predistortion technique is known to be one of the optimum methods due to its relatively low complexity and its convenience for adaptation. In this thesis, different memoryless baseband signal predistortion methods are investigated and analyzed by simulations. Look-Up Table(LUT) and Polynomial approaches are compared and LUT approach is found to be better in performance. Parameters, like indexing, training sequences and training duration are evaluated. An open loop testbench is built with a real amplifier and a different LUT predistortion method that is based on amplifier modeling is offered. It is evaluated by using two tone test and adjacent channel power suppression with 8PSK data. Also, some Look-Up Table parameters are re-investigated with the proposed method. The performances of the proposed method in dierent amplifier classes are observed. Along with these studies, a list of prerequisites for design of a predistortion system is determined.

La Tesis Doctoral estudia la observaci'on en tiempo real de la concentraci'on en el colector de escape de 'oxidos de nitr'ogeno (NOx) y del dosado en motores diesel sobrealimentados (¿ '1 ). Para ello se combinan dos fuentes de informaci'on diferentes: ¿ Sensores capaces de proporcionar una media de dichas variables, ¿ y modelos orientados a control que estiman estas variables a partir de otras medidas del motor. El trabajo parte de la evaluaci'on de la precisi'on de los sensores, realizada mediante la comparaci'on de su medida con la proporcionada por equipos anal'¿ticos de alta precisi'on, que son usados como est'andares de calibraci'on est'atica. Tambi'en se desarrollan en la Tesis m'etodos para la calibraci'on de la din'amica del sensor; dichos m'etodos permiten identi¿car un modelo de comportamiento del sensor y revelar su velocidad de respuesta. En general, estos sensores demuestran ser precisos pero relativamente lentos. Por otra parte, se proponen modelos r'apidos para la estimaci'on de NOx y ¿ '1 . Estos m'etodos, basados en relaciones f'¿sicas, tablas de par'ametros y una serie de correcciones, emplean las medidas proporcionadas por otros sensores con el ¿n de proporcionar una estimaci'on de las variables de inter'es. Los modelos permiten una estimaci'on muy r'apida, pero resultan afectados por efectos de deriva que comprometen su precisi'on. Con el ¿n de aprovechar las caracter'¿sticas din'amicas del modelo y mantener la precisi'on en estado estacionario del sensor, se proponen t'ecnicas de fusi'on de la informaci'on basadas en la aplicaci'on de ¿ltros de Kalman (KF). En primer lugar, se dise¿na un KF capaz de combinar ambas fuentes de informaci'on y corregir en tiempo real el sesgo entre las dos se¿nales. Posteriormente, se estudia la adaptaci'on en tiempo real de los par'ametros del modelo con el ¿n de corregir de forma autom'atica los problemas de deriva asociados al uso de modelos. Todos los m'etodos y procedimientos desarrollados a lo largo de la presente Tesis Doctoral se han aplicado de forma experimental a la estimaci'on de NOx y ¿ '1 . De forma adicional, la Tesis Doctoral desarrolla aspectos relativos a la transferencia de estos m'etodos a los motores de serie. / The dissertation covers the problem of the online estimation of diesel engine exhaust concentrations of NOx and '1. Two information sources are utilised: ¿ on-board sensors for measuring NOx and '1, and ¿ control oriented models (COM) in order to predict NOx and '1. The evaluation of the static accuracy of these sensors is made by comparing the outputs with a gas analyser, while the dynamics are identified on-board by perform- ing step-like transitions on NOx and '1 after modifying ECU actuation variables. Different methods for identifying the dynamic output of the sensors are developed in this work; these methods allow to identify the time response and delay of the sensors if a sufficient data set is available. In general, these sensors are accurate but present slow responses. Afterwards, control oriented models for estimating NOx and '1 are proposed. Regarding '1 prediction, the computation is based on the relative fuel-to-air ratio, where fuel comes from an ECU model and air mass flow is measured by a sensor. For the case of NOx, a set-point relative model based on look-up tables is fitted for representing nominal engine emissions with an exponential correction based on the intake oxygen variation. Different corrections factor for modeling other effects such as the thermal loading of the engine are also proposed. The model is able to predict NOx fast with a low error and a simple structure. Despite of using models or sensors, model drift and sensor dynamic deficiencies affect the final estimation. In order to solve these problems, data fusion strategies are proposed by combining the steady-state accuracy of the sensor and the fast estimation of the models by means of applying Kalman filters (KF). In a first approach, a drift correction model tracks the bias between the model and the sensor but keeping the fast response of the model. In a second approach, the updating of look-up tables by using observers is coped with different versions based on the extended Kalman filter (EKF). Particularly, a simplified KF allows to observe the parameters with a low computational effort. Finally, the methods and algorithms developed in this work are combined and applied to the estimation of NOx and '1. Additionally, the dissertation covers aspects relative to the implementation of the methods in series engines. / Blanco Rodríguez, D. (2013). Modelling and observation of exhaust gas concentrations for diesel engine control [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/32666 / Premios Extraordinarios de tesis doctorales

Dans l'apprentissage machine, la classification est le processus d’assigner une nouvelle observation à une certaine catégorie. Les classifieurs qui mettent en œuvre des algorithmes de classification ont été largement étudié au cours des dernières décennies. Les classifieurs traditionnels sont basés sur des algorithmes tels que le SVM et les réseaux de neurones, et sont généralement exécutés par des logiciels sur CPUs qui fait que le système souffre d’un manque de performance et d’une forte consommation d'énergie. Bien que les GPUs puissent être utilisés pour accélérer le calcul de certains classifieurs, leur grande consommation de puissance empêche la technologie d'être mise en œuvre sur des appareils portables tels que les systèmes embarqués. Pour rendre le système de classification plus léger, les classifieurs devraient être capable de fonctionner sur un système matériel plus compact au lieu d'un groupe de CPUs ou GPUs, et les classifieurs eux-mêmes devraient être optimisés pour ce matériel. Dans ce mémoire, nous explorons la mise en œuvre d'un classifieur novateur sur une plate-forme matérielle à base de FPGA. Le classifieur, conçu par Alain Tapp (Université de Montréal), est basé sur une grande quantité de tables de recherche qui forment des circuits arborescents qui effectuent les tâches de classification. Le FPGA semble être un élément fait sur mesure pour mettre en œuvre ce classifieur avec ses riches ressources de tables de recherche et l'architecture à parallélisme élevé. Notre travail montre que les FPGAs peuvent implémenter plusieurs classifieurs et faire les classification sur des images haute définition à une vitesse très élevée. / In the machine learning area, classification is a process of mapping a new observation to a certain category. Classifiers which implement classification algorithms have been studied widely over the past decades. Traditional classifiers are based on algorithms such as SVM and neural nets, and are usually run by software on CPUs which cause the system to suffer low performance and high power consumption. Although GPUs can be used to accelerate the computation of some classifiers, its high power consumption prevents the technology from being implemented on portable devices such as embedded systems or wearable hardware. To make a lightweight classification system, classifiers should be able to run on a more compact hardware system instead of a group of CPUs/GPUs, and classifiers themselves should be optimized to fit that hardware. In this thesis, we explore the implementation of a novel classifier on a FPGA-based hardware platform. The classifier, devised by Alain Tapp (Université de Montréal), is based on a large amount of look-up tables that form tree-structured circuits to do classification tasks. The FPGA appears to be a tailor-made component to implement this classifier with its rich resources of look-up tables and the highly parallel architecture. Our work shows that a single FPGA can implement multiple classifiers to do classification on high definition images at a very high speed.

The objective of the research was to design and test an SRAM system which can meet the performance criteria for Memory Based Computing (MBC). This form of computing consists of a Look-Up Table (LUT) which is basically memory array mapped with a function; the computations thereafter consist of essentially read operations. An MBC framework requires very fast and low power read operations. Moreover, the cells need to be read stable as major part of the computation is done by reading the LUTs mapped in the SRAM array. Design and measurement of a prototype MBC test-chip with SRAM system optimized for read-heavy applications is presented in this thesis. For this purpose, a prototype MBC system was designed and taped out. Essential study of the write-ability of the core LUT is also presented. The core memory array for function table mapping was characterized for leakage, write-ability and power saving associated with pulsed read mode.

Aquesta Tesi Doctoral se centra en el disseny d'un nou linealitzador de Predistorsió Digital (Digital Predistortion - DPD) capaç de compensar la dinàmica i els efectes no lineals introduïts pels Amplificadors de Potència (Power Amplifiers - PAs). Un dels trets més rellevants d'aquest nou predistorsionador digital i adaptatiu consisteix en ser deduïble a partir d'un model de PA anomenat Nonlinear Auto-Regressive Moving Average (NARMA). A més, la seva arquitectura multi-LUT (multi-Taula) permet la implementació en un dispositiu Field Programmable Gate Array (FPGA).La funció de predistorsió es realitza en banda base, per tant, és independent de la banda freqüencial on es durà a terme l'amplificació del senyal de RF, el que pot resultar útil si tenim en compte escenaris multibanda o reconfigurables. D'altra banda, el fet que aquest DPD tingui en compte els efectes de memòria introduïts pel PA, representa una clara millora de les prestacions aconseguides per un simple DPD sense memòria. En comparació amb d'altres DPDs basats en models més computacionalment complexos, com és el cas de les xarxes neuronals amb memòria (Time-Delayed Neural Networks - TDNN), la estructura recursiva del DPD proposat permet reduir el nombre de LUTs necessàries per compensar els efectes de memòria del PA. A més, la seva estructura multi-LUT permet l'escalabilitat, és a dir, activar or desactivar les LUTs que formen el DPD en funció de la dinàmica que presenti el PA.En una primera aproximació al disseny del DPD, és necessari identificar el model NARMA del PA. Un dels majors avantatges que presenta el model NARMA és la seva capacitat per trobar un compromís entre la fidelitat en l'estimació del PA i la complexitat computacional introduïda. Per reforçar aquest compromís, l' ús d'algoritmes heurístics de cerca, com són el Simulated Annealing o els Genetic Algorithms, s'utilitzen per trobar els retards que millor caracteritzen la memòria del PA i per tant, permeten la reducció del nombre de coeficients necessaris per caracteritzar-la. Tot i així, la naturalesa recursiva del model NARMA comporta que, de cara a garantir l'estabilitat final del DPD, cal dur a terme un estudi previ sobre l'estabilitat del model.Una vegada s'ha obtingut el model NARMA del PA i s'ha verificat l'estabilitat d'aquest, es procedeix a l'obtenció de la funció de predistorsió a través del mètode d'identificació predictiu. Aquest mètode es basa en la continua identificació del model NARMA del PA i posteriorment, a partir del model obtingut, es força al PA perquè es comporti de manera lineal. Per poder implementar la funció de predistorsió en la FPGA, cal primer expressar-la en forma de combinacions en paral·lel i cascada de les anomenades Cel·les Bàsiques de Predistorsió (BPCs), que són les unitats fonamentals que composen el DPD. Una BPC està formada per un multiplicador complex, un port RAM dual que actua com a LUT (taula de registres) i un calculador d'adreces. Les LUTs s'omplen tenint en compte una distribució uniforme dels continguts i l'indexat d'aquestes es duu a terme mitjançant el mòdul de l'envoltant del senyal. Finalment, l'adaptació del DPD consisteix en monitoritzar els senyals d'entrada i sortida del PA i anar duent a terme actualitzacions periòdiques del contingut de les LUTs que formen les BPCs. El procés d'adaptació del contingut de les LUTs es pot dur a terme en la mateixa FPGA encarregada de fer la funció de predistorsió, o de manera alternativa, pot ser duta a terme per un dispositiu extern (com per exemple un DSP - Digital Signal Processor) en una escala de temps més relaxada. Per validar l'exposició teòrica i provar el bon funcionalment del DPD proposat en aquesta Tesi, es proporcionen resultats tant de simulació com experimentals que reflecteixen els objectius assolits en la linealització del PA. A més, certes qüestions derivades de la implementació pràctica, tals com el consum de potència o la eficiència del PA, són també tractades amb detall. / This Ph.D. thesis addresses the design of a new Digital Predistortion (DPD) linearizer capable to compensate the unwanted nonlinear and dynamic behavior of power amplifiers (PAs). The distinctive characteristic of this new adaptive DPD is its deduction from a Nonlinear Auto Regressive Moving Average (NARMA) PA behavioral model and its particular multi look-up table (LUT) architecture that allows its implementation in a Field Programmable Gate Array (FPGA) device.The DPD linearizer presented in this thesis operates at baseband, thus becoming independent on the final RF frequency band and making it suitable for multiband or reconfigurable scenarios. Moreover, the proposed DPD takes into account PA memory effects compensation which representsan step forward in overcoming classical limitations of memoryless predistorters. Compared to more computational complex DPDs with dynamic compensation, such Time-Delayed Neural Networks (TDNN), this new DPD takes advantage of the recursive nature of the NARMA structure to relax the number of LUTs required to compensate memory effects in PAs. Furthermore, its parallel multi-LUT architecture is scalable, that is, permits enabling or disabling the contribution of specific LUTs depending on the dynamics presented by a particular PA.In a first approach, it is necessary to identify a NARMA PA behavioral model. The extraction of PA behavioral models for DPD linearization purposes is carried out by means of input and output complex envelope signal observations. One of the major advantages of the NARMA structure regards its capacity to deal with the existing trade-off between computational complexity and accuracy in PA behavioral modeling. To reinforce this compromise, heuristic search algorithms such the Simulated Annealing or Genetic Algorithms are utilized to find the best sparse delays that permit accurately reproducing the PA nonlinear dynamic behavior. However, due to the recursive nature of the NARMA model, an stability test becomes a previous requisite before advancing towards DPD linearization.Once the PA model is identified and its stability verified, the DPD function is extracted applying a predictive predistortion method. This identification method relies just on the PA NARMA model and consists in adaptively forcing the PA to behave as a linear device. Focusing in the DPD implementation, it is possible to map the predistortion function in a FPGA, but to fulfill this objective it is first necessary to express the predistortion function as a combined set of LUTs.In order to store the DPD function into a FPGA, it has to be stated in terms of parallel and cascade Basic Predistortion Cells (BPCs), which are the fundamental building blocks of the NARMA based DPD. A BPC is formed by a complex multiplier, a dual port RAM memory block acting as LUT and an address calculator. The LUT contents are filled following an uniform spacing procedure and its indexing is performed with the amplitude (modulus) of the signal's envelope.Finally, the DPD adaptation consists in monitoring the input-output data and performing frequent updates of the LUT contents that conform the BPCs. This adaptation process can be carried out in the same FPGA in charge of performing the DPD function, or alternatively can be performed by an external device (i.e. a DSP device) in a different time-scale than real-time operation.To support all the theoretical design and to prove the linearization performance achieved by this new DPD, simulation and experimental results are provided. Moreover, some issues derived from practical experimentation, such as power consumption and efficiency, are also reported and discussed within this thesis.

No description available.

This thesis describes the development and validation of a look-up table capable of predicting heat transfer to water flowing vertically upward in a circular tube in the trans-critical pressure range from 19 to 30 MPa. The table was based on an extensive and screened experimental database and its trends were smoothened to remove unrealistic scatter and physically implausible discontinuities. When compared to other prediction methods, the present look-up table approximated the experimental data closer in values and trends. Moreover, unlike existing prediction methods, the table applies not only to normal heat transfer conditions but also to conditions with heat transfer deterioration and enhancement. A separate multi-fluid look-up table for trans-critical heat transfer was also developed, which besides the existing water database incorporated new measurements in carbon dioxide; the latter were collected at the University of Ottawa supercritical flow loop under conditions of interest for the current Super-Critical Water-Cooled Reactor designs, for which few water data were available in the literature. Existing fluid-to-fluid scaling laws were tested and two additional sets of scaling laws were proposed, which are applicable not only to the supercritical pressure region, but also to the high pressure subcritical region. The multi-fluid table is applicable to water at conditions of normal and abnormal heat transfer, but its applicability to model fluids is restricted to the normal heat transfer mode.